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Some considerations regarding:
Variation on a theme. Part of a conversation. My suggestions regarding a possibly platform. Speculative. Another exercise in higher order cybernetics. Why not.
[This post is mostly part of a conversation I had. But it may be relevant and of some interest to others. Who knows. Therefore here it is posted.]
Word count: 1.500 ~ 6 PAGES | Revised: 2018.11.15
OPEN PROBLEM
Much of science is published in a way most people cannot read. There is a paywall. If they want to read, they must pay.
They can't pay or don't want to pay; it's maybe worth it to them to read for free. But not worth it when it costs so much.
Yet science is a public good [SEI68,LAND69,98]. It time binds. It drives, in the long run, just about everything.
Uh oh.
:_(
Basic and applied science, pure and applied mathematics is often also published with open access. In that case the authors pay fees.
Most scientists do have a roof over their head. They are connected with an institution. Many institutions will subsidize the fees involved. Once the writer fills out a form requesting this.
QUESTION: So how can society experiment with new kinds of science publishing that involves no fees of any kind?
McLuhan, Waddington, Simon, and others discussed this. Predicted this. Said it would happen. Said new technology will soon make it happen.
Soon depends on perspective. Soon depends on the context.
They said it would happen soon. Interesting.
And then, like in most stories of this kind, stories about the prospects of new technology, the next line in the story is something like AND THEN NOTHING HAPPENED FOR FIFTY YEARS.
Hmmm.
And it's really been fifty years.
Now what?
OPTIONS
Blockchains and the category of technologies these fall into provide basically free and neutral and open archiving. Papers, comments, everything. Immutable. Therefore a real archive.
Hmm.
That sounds convenient. It's also important to really increase science participation and the spread of important ideas.
Now what? Now users are needed.
What will attract users?
Most scientists have subsidized open access. What could convince them to submit to new journals?
I suggest the answer is consistency. Something else could be speed when getting longer papers properly reviewed.
The new platforms must be more consistent than most current platforms. Or they will not eat their pie.
Like the best restaurants, consistency and quality in what matters leads to reputation. Which leads to participation. Use. Consumption.
A journal that has both MOST articles worth reading to MOST readers AND also more articles than MOST other journals is considered more consistent.
So? What to do?
Experimental algorithms can be designed.
The usual variations on the higher order cybernetics theme. Layered feedbacks.
But who's going to build even simple test systems ...? There's a definite lack of developers who are both (a) interested in the theme (b) can build complex systems (c) don't need money urgently and can wait and therefore won't take exclusively higher paying jobs. And there are plenty of higher paying jobs than making something in science publishing.
I suggest [WOLF02] is correct that building and testing on data where the results are already known by other means is the best way to find out whether something works for complex systems. But getting freelancers ... even for competitive salaries has lately been ... ah ... challenging.
For complex systems there's sensitive dependence on unexpected correlations. Same reason complex systems are powerful. Double edged sword. Feedbacks. Nobody who funds has yet been convinced of validity based on theory. Tests on a data set needed.
So if rapid prototyping can be done, it's the way to move forward. If not ... the way forward will be rather difficult.
One interest criterion by which a new platform can compete with established players matching high consistency at lower cost.
That will need testing. Another criterion could be speed. Namely speed for getting long papers reviewed and ranked quickly. That means getting a very good smart labeling system, which makes it convenient to read such papers on this platform, rather than just pdf preprint. Long defined as longer than most journals in the respective field quickly publish. That niche is open. Some niches are more open. Others less. But they all require developers. Like business ideas without a company and resources, algorithms are worth almost nothing. It's a lemons problem, how can you signal they work, even if they do? And how do you know for sure they do? Lemons markets typically collapse [AKER62].
Hmm.
OPEN SOLUTION FOR OPEN SCIENCE
Here's an algorithm worth testing.
Or at least meant to suggest there is no shortage of algorithms of a particular type that are worth considering.
Consider a platform that has an option for readers to enter their email and sign up. They can read without doing that.
What papers each unique user reads is noted.
The order is noted.
Length is noted.
Other users incentivized to label papers by similarly. For example, in return for more commenting powers. Moderation powers. Doesn't have to be that. Many options.
Yet other users incentivized, same way, to review quality of similarity labels.
Define a ReadSegment to be the vector of similarity values that is read by a unique user.
Compare each ReadSegment in a fast database with a random walk over similarity values.
Unique users who deviate from a random walk get a popup, on a large indicator appear on the next pages they read in a session, suggesting they sign up for a free account and help review papers/posts.
Should be immutable, free archiving of publications and open reviews and comments. This should be prominently communicated.
Next use similarity values to assign reputations in fields.
There is no single reputation. But tokens for each reputation. Low and middling reputation allows only contributing similarity values and labels, and lightly weighs these labels.
Higher reputations allow most powerful moderation features. Users therefore have a distribution for their reputation. 50 for similarity coordinate bounds 7562 to 9435, 0 for 2470 to 5400, 25 for 12400 to 25600. They also have utility tokens that open system powers. If they want to do something more on posts that were labeled 10384 to 18256, they can trade 2 of their tokens for 7562 to 9435 for 1 token in 10384 to 18256. (Some other coordinate system may be used. Functors which map papers to labels and labeling processes to equivalent "labeling processes" worth signing up.)
For linear mixes, system averages the "price". Or something else, but fairly defined. And so on.
Get some people, give each some money, and let them play the system for a week. Like in experimental economics. This to set initial labels for a test data set.
After that open to wider sets of users and see what happens. Check not so much quality, but consistency. Once consistent results, one knows what parameters to vary to bias in favor of good quality.
Boundary conditions probably matter. Initial conditions probably matter. Select the initial group carefully. But open to a broader set of users quickly.
Test. Improve. And so on.
Get the ball rolling.
ABOUT ME
I'm a scientist who writes science fiction under various names.
The magazines that I most recommend: Magazine of fantasy and science fiction, Compelling science fiction, Writers of the future, . . .
The magazines that I most recommend: Magazine of fantasy and science fiction, Compelling science fiction, Writers of the future, . . .
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Somewhere at the very top of the text above I put a tag: — Revised: Date.
Leave comments below, with suggestions.
Maybe points to discuss. — As time permits.
Guess what? Meanwhile the length may've doubled . . . ¯\ _ (ツ) _ /¯ . . .
2018.11.15 — POSTED — WORDS: 1.500
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TrufflePigThere are alternatives to traditional journals which you may not have factored into your analysis. Search for the Public Library of Science (PLoS) and you will find professionally peer reviewed papers published on a publicly accessible (no paywall) site online.
There remain additional reasons that traditional journals, and even peer review, inhibit the development and publishing of good science and papers, specifically mechanisms employed to prevent demonetization of extant grantees by upstarts, political machination, and bias. Algorithms will combat the latter, but not the former faults with extant publishing methodologies. I'd like to see more concern given integrity in science, as I have experienced some of these impediments in my work, and feel they have prevented far more than my own meager contributions from being available to the world.
Good science gets crushed sometimes by people whose incomes depend on the status quo, and this is bad for society. A side effect is also that 'mad scientists' and frauds are able to undertake work contrary to the public interest in the non-traditional publishing mechanisms that must eventuate due to the failure of extant mechanisms to appropriately and functionally publish. Some people are presently alarmed, and even with some good reason, at the biohacking movement, makers, and other somewhat renegade publishing that has arisen as a result of censorship, gatekeeping, and other failures of scientific publishing to function nominally.
Thanks!
Good points. The reason some of the nonpaywall journal options are not a huge game changer is that they have high article processing fees.
[Will add most of this comment to a section in the original post.]
-4-
The reason some of the nonpaywall journal options are not a huge game changer, despite some being consistent, is that they have high article processing fees. Therefor they cannot defeat twigging.
(The real purpose. Science works less effectively when there is twigging. Where there is little reading. Rapid technical progress involves the community reading what comes out, more if more, less if less, not reading less and more narrowly, like a tree grows while the branches all remain the same thickness, if more comes out. That defeats the whole process. Adam Smith made science his first example of division of labor. Each does their part, and all communicate with all, and each knows more easily what any knows, doing only part of the work. Besides being specialized and doing it more accurately and faster. Specialization only works when it is not too narrow specialization. Else the Russell joke, that the specialist knows more and more about less and less and the generalist less and less about more and more becomes true and the game ends in defeat.)
The most significant and interesting achievement would be algorithms and automation such that neither a paywall nor publication or processing fees are necessary. Just peer reviewers and submissions and readers.
Costs and hassles and complexity are troublesome because they naturally reduce participation by everybody. Writers. Readers.
Yet this is a feedback system. It's fundamentally statistical.
When few readers, few reviewers, reviews of some sort are not publications themselves (exceptions being Mathematical Reviews and Behavioral and brain sciences), it's hard to get both speed. No speed, no interest, no numbers. Then no numbers, no interest, no speed.
One way to do things is to have peer review control rank and not worry about the rest. Less useful text should ideally move up and then way down. Papers that are more relevant and recent and good should consistently appear higher, with less time wasted. Reviews would be published and archived. Along with reviews of reviews. Revisions.
Some conjectures published before performing and publishing the correspond experimental work would be quite useful. Improve quality.
Papers could generally be published in pieces. Most people don't read long text anyway. Fortunately or unfortunately.
One issue in all fields is that most of those who do research like doing research more than writing about it and communicating it. But if they are more sloppy in their formal style, papers risk being rejected even if important results.
I find that people are quite a bit more excited about writing up what they are doing publishing sooner rather than later if it is Monday and they can submit the Introduction and Conclusion and References Thursday this week. Then most of the remainder Tuesday next week. And then diagrams Sunday the week after that.
Now that would be a game changer. It could change the statistics of publishing. And mode quality of the results. More eyes watching because new things could and would come out each day. If reviews are easy to post, far less hassle, and archived, and reviews of reviews have meaning, all the way down, and search tools, ... game changer. Today that just means reading arXiv, bioRxiv, basically.
How to deliver value by saving time for peer reviewers and readers. Which brings in more reviewers and readers. More eyes should see papers and check the arguments. Rashevsky said it decades ago, no royal road without reading and reviewing. Meanwhile twigging is the rain which has turned the road into mud.
And so the question becomes algorithms and developers. How to get an order of magnitude more readers and reviewers for manuscripts.
If you know developers who are interested in doing something, let's talk.
For example, this post took me a minute to type up. Had it taken me much more than that, more clicks involved, perhaps it would not been written. Reading a book at the same time. And so on.
[Another section for a revised post.]
-5-
When testing anything with students don't forget to use real value rewards beyond an insignificance threshold. Then it can be published in most journals that accept experimental economics. (Else the data can be rejected as nonrepresentitive by journals where you would like to publish, if that is a goal.)
The following is just to formulate the problem most correctly. We want to create algorithms that are solutions for the real problem.
Which problem is that? Science publishing is mostly one of a typical lemons market. And not only because a randomly selected individual in the world far more often than not cannot distinguish good product from bad product.
So there are two routes to discovering how to make publishing with no fees anywhere and therefore more participants all around.
(1) Raising awareness by writing about it and publishing in appropriate places. Minus side is most will be paywalled. But it will give what [KITCH95] (Philip KITCHER, The advancement of science, Oxford: University Press, 1995) lovingly called unearned credibility. A paper in econometrica has more umph for getting funding than just talking about it, or publishing on a blog or in another journal. Even if same text. Because most, even fellow travelers, will probably not read it.
(2) Just making a product. Linux style. Very minimal.
How can the prospective user trust that your algorithm will produce consistent quality going on top and low quality dropping to the bottom (in journals this would just be Advice to Reject) therefore not damaging to their reputation if submitting to it? Considering that this depends not only on the algorithm and the best papers submitted to it by authors, but also the worst papers, which will affect the perceived status of the platform, it's Umph, Impact, Etc, even if some papers are excellent.
And considering that most do not and will not read papers [KITCH95]. Some skim. Most don't even skim. So randomly selecting a user, they will judge a paper published based on the platform. Not on the content of the paper.
That in turn affects perception of the platform.
Which exposed good papers to risk. The risk is, at least, that of not getting read despite having done actually good work. (Not getting read when not doing good work is not a problem and not a risk.)
So because the quality of a publishing platform does not depend entirely or even mostly on its structure, it's hard to distinguish platforms that are good from those which are not so good. In the sense of what they do that does contribute largely to a good or bad outcome. Furthermore, at lower granularity, it's therefore hard to distinguish publishing platform that will consistently deliver high quality, when just launched, even if the design is known, even if the design is really good, from publishing platforms that will not consistently deliver high quality.
So even an excellent algorithm needs testing in a very public way, however that is done, because the payoff does not depend only on it.
Meanwhile it must be very convenient to get participation; only a few clicks to submitting a review or something. Like with the latest editorialmanager stuff. No longer do you have to make a password or log in, once invited; just get a link, editor generates pass, sends in email, automated, and can be take you straight to the submit decision page, and meanwhile you can dl the manuscript you are asked to review without logging in anywhere.
Leading to the conclusion that some fun factor is required in the long run. Something outside the dilemma. And algorithms which bring a new level of convenience, while being consistent. Something outside the dilemma.
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